Characterizing the Physiology of Circulatory Arrest in Humans

Introduction

This document is meant to provide rationale for performing data analysis and statistical analysis for all data presented in Characterizing the Physiology of Circulatory Arrest in Humans. The code presented here is meant to be a single file that with the 3 data file dependencies (main_data.xlsx, time_series_signals.csv, timing.csv), which will provide all logic behind how figures were created and how all statistics were run. As this code was developed by JDB from 2023 onwards, some library dependencies in R may alter the output of the data. As such, all library versions are herein reported to be able to hopefully provide readers an easy avenue to replicate the supported data as shared on figshare. Please let Dr. Mypinder Sekhon or myself know if you have any questions or concerns.

Dependencies

R Library	Version Number
\(R\)	v4.3.1
\(tidyverse\)	v2.0.0
\(rstatix\)	v0.7.2
\(lme4\)	v1.1-34
\(ggplot2\)	v3.5.1
\(ggrepel\)	v0.9.6
\(ggpubr\)	v0.6.0
\(markdown\)	v1.13
\(readxl\)	v1.4.3
\(kableExtra\)	v1.4.0
\(dplyr\)	v1.1.3

1 Demographics

Sex of the Critically Ill Patient Cohort (N = 32) & Medical Assistance in Dying Cohort (N=3)

Sex	Count	Prevalence
Male	28	80%
Female	7	20%

Diagnosis Prevalence in the Critically Ill Patient Cohort (N = 32)

Diagnosis	Count	Prevalence
HIBI	15	46.88%
Sepsis	6	18.75%
TBI	6	18.75%
ICH	4	12.5%
SAH	1	3.12%

Continuous Variables for Critically Ill Patient Cohort

variable	n	min	max	median	q1	q3	iqr	mad	mean	sd	se	ci
Age	30	32.000	87.000	63.500	50.000	68.000	18.000	10.378	59.300	13.593	2.482	5.076
SaO2	30	88.000	97.000	96.000	94.000	96.000	2.000	1.483	95.067	2.016	0.368	0.753
SjvO2	26	54.000	91.000	80.500	76.250	86.750	10.500	8.896	77.923	11.457	2.247	4.628
SO2_av	26	5.000	42.000	14.500	8.500	20.750	12.250	9.637	17.115	11.262	2.209	4.549
PaO2	30	70.000	232.000	100.500	89.500	111.750	22.250	17.050	104.400	29.073	5.308	10.856
PjvO2	20	32.000	71.000	46.000	40.000	56.750	16.750	14.085	48.200	11.723	2.621	5.487
PO2_av	20	25.000	187.000	49.000	36.000	71.250	35.250	24.463	58.850	36.555	8.174	17.108
PaCO2	30	26.000	47.000	36.500	33.250	38.750	5.500	4.448	36.633	4.867	0.889	1.817
PjvCO2	20	33.000	52.000	41.500	37.750	44.500	6.750	5.930	41.650	5.631	1.259	2.636
PCO2_av	20	-13.000	0.000	-3.500	-7.000	-2.000	5.000	3.706	-4.350	3.438	0.769	1.609
HCO3_a	30	9.968	30.847	23.242	21.875	25.675	3.799	3.027	22.493	4.868	0.889	1.818
HCO3_jv	20	16.430	30.418	24.820	22.156	26.919	4.763	3.351	24.374	3.803	0.850	1.780
HCO3_av	20	-4.514	1.950	-0.521	-1.723	0.453	2.176	1.485	-0.688	1.620	0.362	0.758
pH_a	30	7.090	7.490	7.425	7.392	7.450	0.058	0.037	7.402	0.091	0.017	0.034
pH_jv	20	7.320	7.460	7.390	7.350	7.420	0.070	0.044	7.388	0.039	0.009	0.018
pH_av	20	-0.050	0.080	0.030	0.020	0.053	0.032	0.015	0.036	0.028	0.006	0.013
Hb	30	65.000	139.000	91.000	81.750	101.500	19.750	14.826	94.133	17.502	3.195	6.535
Lactate	30	0.500	27.000	1.000	0.800	1.550	0.750	0.445	3.003	5.666	1.034	2.116
Creatinine	30	34.000	492.000	84.500	57.500	151.000	93.500	57.821	123.267	105.765	19.310	39.493
Troponin	24	4.000	9598.000	89.000	27.500	424.000	396.500	117.867	1081.125	2543.997	519.291	1074.236
Bilirubin	29	5.000	146.000	8.000	6.000	15.000	9.000	4.448	23.069	35.901	6.667	13.656
Albumin	28	12.000	33.000	23.500	21.000	26.250	5.250	3.706	23.071	4.578	0.865	1.775
Oxygenation Index	30	2.236	23.790	7.389	5.072	8.588	3.517	3.396	8.242	4.904	0.895	1.831
PETCO2	28	17.250	39.750	30.848	27.281	32.738	5.456	3.947	29.451	4.888	0.924	1.895
DS_fraction	28	1.923	53.833	14.669	9.634	30.855	21.221	14.368	20.359	13.869	2.621	5.378
PF_ratio	30	124.000	532.000	287.143	210.714	327.857	117.143	88.956	287.296	99.419	18.151	37.124
Vasopressors	30	0.000	120.000	0.000	0.000	5.000	5.000	0.000	7.667	22.640	4.134	8.454
Midazolam	30	0.000	15.000	3.500	0.000	5.750	5.750	5.189	4.100	4.029	0.736	1.504
Hydromorphone	30	0.000	2.000	0.500	0.000	1.000	1.000	0.741	0.705	0.719	0.131	0.268
Midazolam_bolus	30	0.000	10.000	0.000	0.000	0.000	0.000	0.000	1.067	2.420	0.442	0.904
Hydromorphone_bolus	30	0.000	6.000	0.000	0.000	2.750	2.750	0.000	1.200	1.937	0.354	0.723
MCA	29	28.270	110.100	46.560	33.270	60.590	27.320	19.733	51.363	20.914	3.884	7.955
PCA	29	12.840	84.580	32.080	27.540	40.210	12.670	9.296	36.455	15.606	2.898	5.936
SvO2	15	32.480	78.900	66.880	62.130	72.505	10.375	8.777	65.314	11.795	3.046	6.532
CO	15	2.763	9.450	6.390	5.169	7.655	2.486	1.868	6.371	1.973	0.509	1.093
CVP	27	1.880	21.670	10.020	7.535	13.565	6.030	4.566	10.325	5.251	1.011	2.077
MAP	30	55.140	118.900	74.750	64.690	92.197	27.507	17.732	79.841	18.370	3.354	6.860
ICP	29	4.000	37.000	15.000	11.000	19.000	8.000	5.930	15.930	7.764	1.442	2.953
CPP	29	27.590	98.290	61.680	52.950	76.600	23.650	17.228	64.763	17.249	3.203	6.561
MCA_FVD	29	13.950	79.570	29.090	20.350	35.970	15.620	12.958	32.155	16.183	3.005	6.156
HR	30	56.000	126.000	86.000	78.750	95.500	16.750	13.343	87.700	15.892	2.902	5.934
ECHO_LVEF	16	20.000	65.000	57.000	40.250	65.000	24.750	11.861	50.500	15.122	3.780	8.058
FIO2	30	25.000	75.000	35.000	30.000	40.000	10.000	7.413	38.000	10.875	1.986	4.061
PEEP	30	5.000	12.000	8.000	5.000	10.000	5.000	2.965	7.767	2.445	0.446	0.913
PSV	30	5.000	14.000	8.000	5.000	10.000	5.000	3.706	8.267	2.852	0.521	1.065
Mv	30	6.840	11.040	9.000	8.155	9.735	1.580	1.186	8.977	1.101	0.201	0.411
Tv	30	380.000	610.000	460.000	421.250	497.500	76.250	59.304	467.833	54.562	9.962	20.374
RR	30	15.000	24.000	19.500	17.250	21.000	3.750	2.965	19.333	2.631	0.480	0.982
a HCO3- values were calculated from the Henderson-Hasselbach equation using PCO2 and pH values.
b Physiologic Dead Space was calculated as per Bohr’s Method from West’s Respiratory Physiology 11th Edition.
c av is for arterial-to-jugular venous gradients denoting changes that occur across the brain.

2 Main Text Data

FIGURES

Figure 1

Do the MCA and PCAv have a difference in timing from when they give out?

The time from WLST to cessation of PCAv was less than the time from WLST to cessation of MCAv (N = 28, r = 0.51, P = 0.01) with a large effect size.

Figure 2

Figure 3

Selection of Linear Mixed Effects Models after Comparison for rSO2.

	npar	AIC	BIC	logLik	deviance	Chisq	Df	Pr(>Chisq)
MODEL1	4	21753.04	21777.26	-10872.52	21745.04	NA	NA	NA
MODEL2	4	23028.27	23052.49	-11510.13	23020.27	0.000	0	NA
MODEL3	6	21398.50	21434.84	-10693.25	21386.50	1633.766	2	0
a Model 1 - Random Intercept
b Model 2 - Random Slope
c Model 3 - Random Slope and Random Intercept (Correlated)

Data Processing Notes:
For linear mixed-effects models, we can choose either random intercepts (Model 1), random slopes (Model 2), or random slopes and random intercepts (Model 3). Visually, by just plotting the data we see that there is heterogeneity of slopes which is not surprising from a physiologic perspective so Model 2 and Model 3 seem more likely that they would fit. We also don’t necessarily think that the intercepts will be identical between patients as perhaps cerebral blood velocities cease while there is still some presence of MAP (which we did eventually find). As such, Model 3 was the intuitively most accurate from a physiologic perspective when we first started considering this data.

Selection of Linear Mixed Effects Models after Comparison for MCAv.

	npar	AIC	BIC	logLik	deviance	Chisq	Df	Pr(>Chisq)
MODEL1	4	32246.45	32271.80	-16119.23	32238.45	NA	NA	NA
MODEL2	4	31337.90	31363.25	-15664.95	31329.90	908.5477	0	NA
MODEL3	6	31191.75	31229.78	-15589.88	31179.75	150.1523	2	0
a Model 1 - Random Intercept
b Model 2 - Random Slope
c Model 3 - Random Slope and Random Intercept (Correlated)

Selection of Linear Mixed Effects Models after Comparison for PCAv.

	npar	AIC	BIC	logLik	deviance	Chisq	Df	Pr(>Chisq)
MODEL1	4	25187.86	25212.42	-12589.93	25179.86	NA	NA	NA
MODEL2	4	24949.65	24974.21	-12470.82	24941.65	238.2079	0	NA
MODEL3	6	24687.37	24724.21	-12337.68	24675.37	266.2814	2	0
a Model 1 - Random Intercept
b Model 2 - Random Slope
c Model 3 - Random Slope and Random Intercept (Correlated)

As MCAv and PCAv have different starting baselines, is there an interaction effect when baselines are normalized to 100% of CBv going down to 0% CBv?

Linear Mixed Models for whether there is an interaction between the MCAv and PCAv for MAP responses.

term	npar	sumsq	meansq	statistic
ABP	1	0.0080909	0.0080909	67.066312
Vessel	1	0.0001516	0.0001516	1.256289
ABP:Vessel	1	0.0001355	0.0001355	1.123480

Figure 4

Figure 5

Data Processing Notes:
Tau and UCH-L1 were not linear over the full dilution range (4x, 20x, 100x). We had internal discussion about whether we only include Tau and UCH-L1 data at 4x, but we decided that as paired samples were run at the same dilution and our main question whether their are changes from Pre-WLST to Post-WLST, we decided to keep all dilution levels. There is pseudocode embedded in this RMarkdown to re-run the analysis without the higher dilutions for Tau and UCH-L1 (see Serum Proteomics code chunk). The positives of keeping the extra dilution data is the higher sample N for comparison for paired analysis.

Serum blood-based neurologic biomarker comparisons using the Quanterix Argo HT platform. Red is for arterial, blue is for jugular venous, and purple is for cerebral A-V gradients.

---

Figure 5S. Arterial plasma proteomic comparisons between healthy control participants and critically ill patients prior to withdrawal of life-sustaining treatment.

Figure 5T. Arterial plasma proteomic comparisons between critically ill patients prior to withdrawal of life-sustaining treatment and immediately after systolic blood pressure dropped below 60 mmHg.

Figure 5T. Arterial plasma proteomic comparisons between critically ill patients prior to withdrawal of life-sustaining treatment and immediately after systolic blood pressure dropped below 60 mmHg.

Figure 6

Figure 6. Physiologic responses to the last agonal breath during the dying process in critically ill humans

TABLES

Timing Data

Time from WLST to Cessation of Cerebral Blood Velocities and Pulseless Electrical Activity

ID	WLST to MCA	WLST to PCA	WLST to PEA	MCA to PEA (s)	PCA to PEA (s)
Sepsis 1	380	380	389	9	9
HIBI 2	2105	2101	2106	1	5
HIBI 3	2599	2520	2644	45	124
HIBI 4	2648	2628	2750	102	122
HIBI 5	1160	1250	1312	152	62
TBI 6	11582	11582	11640	58	58
HIBI 7	415	415	450	35	35
HIBI 8	12329	12339	12481	152	142
Sepsis 9	4376	4373	4883	507	510
HIBI 10	11847	11791	11844	-3	53
HIBI 11	1852	1530	2179	327	649
HIBI 12	521	521	533	12	12
ICH 13	NA	NA	NA	NA	NA
Sepsis 14	11450	11374	11515	65	141
TBI 15	7817	7906	9945	2128	2039
HIBI 16	22185	21735	22500	315	765
ICH 17	3835	540	4085	250	3545
TBI 18	7380	7380	7625	245	245
Sepsis 19	467	NA	738	271	NA
ICH 20	13990	13990	14030	40	40
HIBI 21	2390	2165	2396	6	231
Sepsis 22	228	228	238	10	10
SAH 23	710	230	769	59	539
HIBI 24	6587	6463	6829	242	366
HIBI 25	NA	NA	NA	NA	NA
ICH 26	4570	3550	4620	50	1070
TBI 27	2164	2164	2300	136	136
TBI 28	1685	1575	1820	135	245
HIBI 29	6185	5992	7815	1630	1823
TBI 30	NA	NA	NA	NA	NA
Sepsis 31	1905	2424	2839	934	415
HIBI 32	1572	825	1748	176	923
MAID 1	969	769	1024	55	255
MAID 2	250	250	277	27	27
MAID 3	167	167	174	7	7

Group Summary Data - Seconds

variable	n	min	max	median	q1	q3	iqr	mad	mean	sd	se	ci
WLST to MCA (s)	13	415	22185	2599	1852	6587	4735	3080.843	5601.769	6372.885	1767.520	3851.096
WLST to PCA (s)	13	415	21735	2520	1530	6463	4933	2963.717	5496.154	6293.968	1745.633	3803.407
WLST to PEA (s)	13	450	22500	2644	2106	7815	5709	3129.769	5833.769	6450.279	1788.986	3897.865
WLST to EA (s)	13	536	23220	2753	2231	8146	5915	2074.157	6115.846	6492.654	1800.738	3923.472
WLST to SBP 60 (s)	13	205	22125	2370	1789	6580	4791	2980.026	5355.769	6277.169	1740.973	3793.255
MCA to PEA (s)	13	-3	1630	102	12	242	230	142.330	232.000	436.077	120.946	263.519
PCA to PEA (s)	13	5	1823	124	53	366	313	158.638	337.615	507.752	140.825	306.832
MCA to EA (s)	13	6	1961	194	126	708	582	265.385	514.077	614.175	170.341	371.142
PCA to EA (s)	13	15	2154	233	142	1030	888	293.555	619.692	686.150	190.304	414.636
a The average time it took for WLST to PEA <5 mmHg was: 82 minutes.
b The average time it took for WLST to no MCA signal was: 77 minutes.
c The average time it took for WLST to no PCA signal was: 76 minutes.

Brain pathology

Data Processing Notes:
The pathology data is scored as ranked categories which presents some challenges when there are a limited number of brain autopsies to analyze. As such, for this initial manuscript, the brain autopsy data was binary classified as either the presence of pathology or absence of pathology. The chronic neuropathology is classified as None, Mild, Moderate, and Severe while the acute inflammation neuropathology is classified as None, Very Mild, Mild, Moderate, and Severe. An alternative approach to analysis in the future would be using Kruskal-Wallis comparisons with a Dunn’s test post hoc to determine if there are differences between different levels of pathology present.

acute_inflammation	n
Mild	12
Moderate	6
None	6
Severe	2
Very Mild	2

chronic_severity	n
Mild	10
Moderate	8
None	8
Severe	2

Cohort	variable	n	min	max	median	q1	q3	iqr	mad	mean	sd	se	ci
Critically Ill Patient Cohort	weight	28	942	1811	1387.5	1286.25	1459.25	173	150.484	1388.643	180.818	34.171	70.114

[1] 28

acute	n
No	6
Yes	22

chronic	n
No	8
Yes	20

n	estimate	p	conf.low	conf.high	method	alternative	p.signif
28	1.31925	1	0.0956354	12.44587	Fisher’s Exact test	two.sided	ns

n	statistic	p	df	method	p.signif
35	0	1	1	Chi-square test	ns

Relationships between the presence of brain neuropathology and various time intervals during the dying process.

Pathology	Duration	P-Value
Chronic Neuropathology	SPO2_70_to_PEA	0.0618
Chronic Neuropathology	SPO2_70_to_ASYS	0.0800
Chronic Neuropathology	SBP50_to_PEA	0.0849
Chronic Neuropathology	SPO2_80_to_ASYS	0.1150
Acute Inflammation Neuropathology	SBP50_to_PEA	0.1360
Chronic Neuropathology	MAP60_to_ASYS	0.1440
Chronic Neuropathology	MAP60_to_PEA	0.1600
Chronic Neuropathology	SBP60_to_ASYS	0.1650
Chronic Neuropathology	SPO2_80_to_PEA	0.1730
Chronic Neuropathology	SBP50_to_ASYS	0.1960
Chronic Neuropathology	MCA_to_PEA	0.2110
Chronic Neuropathology	MCA_to_EA	0.2380
Acute Inflammation Neuropathology	SPO2_70_to_PEA	0.2420
Acute Inflammation Neuropathology	SPO2_80_to_PEA	0.2730
Chronic Neuropathology	MAP50_to_ASYS	0.3110
Chronic Neuropathology	WLST_to_EA	0.3380
Chronic Neuropathology	SBP60_to_PEA	0.3670
Chronic Neuropathology	WLST_to_PEA	0.3670
Chronic Neuropathology	WLST_to_PCA	0.3970
Chronic Neuropathology	WLST_to_VE	0.4040
Acute Inflammation Neuropathology	MCA_to_PEA	0.4470
Acute Inflammation Neuropathology	PCA_to_PEA	0.4470
Chronic Neuropathology	PCA_to_PEA	0.4530
Acute Inflammation Neuropathology	SPO2_80_to_ASYS	0.4570
Chronic Neuropathology	WLST_to_SBP50	0.4610
Acute Inflammation Neuropathology	SPO2_70_to_ASYS	0.4940
Acute Inflammation Neuropathology	WLST_to_SPO2_80	0.4940
Chronic Neuropathology	WLST_to_MCA	0.4950
Chronic Neuropathology	WLST_to_SPO2_80	0.4950
Chronic Neuropathology	PCA_to_EA	0.5310
Acute Inflammation Neuropathology	MCA_to_EA	0.5330
Acute Inflammation Neuropathology	SBP50_to_ASYS	0.5330
Acute Inflammation Neuropathology	SBP60_to_PEA	0.5330
Chronic Neuropathology	MAP50_to_PEA	0.5670
Acute Inflammation Neuropathology	MAP50_to_ASYS	0.5730
Acute Inflammation Neuropathology	MAP50_to_PEA	0.6140
Acute Inflammation Neuropathology	PCA_to_EA	0.6140
Acute Inflammation Neuropathology	WLST_to_SPO2_70	0.6920
Acute Inflammation Neuropathology	WLST_to_MAP50	0.7000
Acute Inflammation Neuropathology	WLST_to_MAP60	0.7000
Acute Inflammation Neuropathology	WLST_to_PCA_diastolic	0.7000
Chronic Neuropathology	WLST_to_MAP50	0.7240
Chronic Neuropathology	WLST_to_MCA_diastolic	0.7240
Acute Inflammation Neuropathology	SBP60_to_ASYS	0.7380
Acute Inflammation Neuropathology	WLST_to_MCA_diastolic	0.7440
Chronic Neuropathology	WLST_to_MAP60	0.7650
Chronic Neuropathology	WLST_to_SBP60	0.8070
Acute Inflammation Neuropathology	MAP60_to_PEA	0.8360
Acute Inflammation Neuropathology	WLST_to_SBP50	0.8360
Acute Inflammation Neuropathology	WLST_to_SBP60	0.8360
Chronic Neuropathology	WLST_to_SPO2_70	0.8680
Acute Inflammation Neuropathology	WLST_to_VE	0.8790
Acute Inflammation Neuropathology	WLST_to_EA	0.8820
Chronic Neuropathology	WLST_to_PCA_diastolic	0.8920
Acute Inflammation Neuropathology	WLST_to_MCA	0.9290
Acute Inflammation Neuropathology	WLST_to_PCA	0.9290
Acute Inflammation Neuropathology	WLST_to_PEA	0.9290
Acute Inflammation Neuropathology	MAP60_to_ASYS	0.9760

Relationships between the presence of brain neuropathology and Blood-Based Neurologic Biomarkers.

Pathology	Biomarker	Vessel	P-Value
Acute Inflammation Neuropathology	Nf-L	Jugular	0.0322
Acute Inflammation Neuropathology	Nf-L	Arterial	0.0480
Chronic Neuropathology	Tau	Jugular	0.1150
Chronic Neuropathology	Tau	Arterial	0.1290
Acute Inflammation Neuropathology	GFAP	Jugular	0.1300
Acute Inflammation Neuropathology	GFAP	Arterial	0.1720
Acute Inflammation Neuropathology	UCH-L1	Arterial	0.6070
Acute Inflammation Neuropathology	UCH-L1	Jugular	0.6070
Chronic Neuropathology	UCH-L1	Arterial	0.6830
Chronic Neuropathology	Nf-L	Jugular	0.7650
Chronic Neuropathology	UCH-L1	Jugular	0.8490
Acute Inflammation Neuropathology	Tau	Jugular	0.8640
Chronic Neuropathology	GFAP	Jugular	0.8920
Chronic Neuropathology	Nf-L	Arterial	0.9350
Chronic Neuropathology	GFAP	Arterial	0.9780
Acute Inflammation Neuropathology	Tau	Arterial	1.0000

Heart pathology

variable	n	min	max	median	q1	q3	iqr	mad	mean	sd	se	ci
weight	22	326	918	421.5	377	444	67	55.597	446.773	135.708	28.933	60.17

Relationships between time intervals during the dying process and presence of heart pathology

CONDITION	Time Interval	group1	group2	n1	n2	statistic	p
Coronary Artery Stenosis	WLST_to_EA	No	Yes	11	9	85	0.00566
Coronary Artery Stenosis	WLST_to_PEA	No	Yes	11	9	85	0.00566
Coronary Artery Stenosis	WLST_to_MCA	No	Yes	11	9	82	0.01250
Left Anterior Descending Artery Stenosis	WLST_to_EA	No	Yes	13	7	76	0.01450
Left Anterior Descending Artery Stenosis	WLST_to_PEA	No	Yes	13	7	76	0.01450
Left Anterior Descending Artery Stenosis	WLST_to_MCA	No	Yes	13	7	75	0.01860
Right Coronary Artery Stenosis	WLST_to_EA	No	Yes	12	8	78	0.02010
Right Coronary Artery Stenosis	WLST_to_PEA	No	Yes	12	8	78	0.02010
Coronary Artery Stenosis	WLST_to_PCA	No	Yes	11	8	71	0.02590
Left Anterior Descending Artery Stenosis	WLST_to_PCA	No	Yes	13	6	63	0.03650
Right Coronary Artery Stenosis	WLST_to_MCA	No	Yes	12	8	75	0.03870
Right Coronary Artery Stenosis	WLST_to_PCA	No	Yes	12	7	64	0.06830
Circumflex Stenosis	WLST_to_PCA	No	Yes	16	3	29	0.63400
Circumflex Stenosis	WLST_to_PEA	No	Yes	17	3	30	0.68900
Myocardial Pathology	WLST_to_MCA	No	Yes	11	9	44	0.71000
Circumflex Stenosis	WLST_to_EA	No	Yes	17	3	29	0.76500
Circumflex Stenosis	WLST_to_MCA	No	Yes	17	3	29	0.76500
Myocardial Pathology	WLST_to_EA	No	Yes	11	9	45	0.76600
Left Ventricle Pathology	WLST_to_PEA	No	Yes	12	8	52	0.79200
Ventricular Pathology	WLST_to_PEA	No	Yes	12	8	52	0.79200
Myocardial Pathology	WLST_to_PEA	No	Yes	11	9	46	0.82400
Left Ventricle Pathology	WLST_to_MCA	No	Yes	12	8	51	0.85100
Ventricular Pathology	WLST_to_MCA	No	Yes	12	8	51	0.85100
Right Atrium Pathology	WLST_to_EA	No	Yes	18	2	16	0.85300
Right Atrium Pathology	WLST_to_MCA	No	Yes	18	2	16	0.85300
Left Atrium Pathology	WLST_to_EA	No	Yes	18	2	16	0.85300
Left Atrium Pathology	WLST_to_MCA	No	Yes	18	2	16	0.85300
Right Ventricle Pathology	WLST_to_EA	No	Yes	18	2	16	0.85300
Right Ventricle Pathology	WLST_to_MCA	No	Yes	18	2	16	0.85300
Atrial Pathology	WLST_to_EA	No	Yes	18	2	16	0.85300
Atrial Pathology	WLST_to_MCA	No	Yes	18	2	16	0.85300
Left Ventricle Pathology	WLST_to_EA	No	Yes	12	8	50	0.91000
Ventricular Pathology	WLST_to_EA	No	Yes	12	8	50	0.91000
Right Atrium Pathology	WLST_to_PEA	No	Yes	18	2	17	0.94700
Left Atrium Pathology	WLST_to_PEA	No	Yes	18	2	17	0.94700
Right Ventricle Pathology	WLST_to_PEA	No	Yes	18	2	17	0.94700
Atrial Pathology	WLST_to_PEA	No	Yes	18	2	17	0.94700
Myocardial Pathology	WLST_to_PCA	No	Yes	10	9	45	1.00000
Right Atrium Pathology	WLST_to_PCA	No	Yes	17	2	17	1.00000
Left Atrium Pathology	WLST_to_PCA	No	Yes	17	2	17	1.00000
Right Ventricle Pathology	WLST_to_PCA	No	Yes	17	2	17	1.00000
Left Ventricle Pathology	WLST_to_PCA	No	Yes	12	7	42	1.00000
Ventricular Pathology	WLST_to_PCA	No	Yes	12	7	42	1.00000
Atrial Pathology	WLST_to_PCA	No	Yes	17	2	17	1.00000

Baseline Data Prior to WLST

Baseline variables prior to withdrawal of life sustaining treatments

variable	n	min	max	median	q1	q3	iqr	mad	mean	sd	se	ci
Mean Arterial Pressure	29	40.090	114.082	81.925	66.394	89.837	23.443	20.983	80.296	18.318	3.401	6.968
Peripheral Oxygen Saturation	28	69.417	100.000	98.035	95.967	99.017	3.049	1.927	96.400	5.725	1.082	2.220
Middle Cerebral Artery Blood Velocity	26	24.294	112.744	50.826	32.724	63.269	30.545	23.837	52.570	22.913	4.494	9.255
Posterior Cerebral Artery Blood Velocity	25	13.457	84.115	39.074	29.405	44.557	15.152	12.482	40.164	15.325	3.065	6.326
Systolic Blood Pressure	22	91.165	175.753	113.937	100.679	128.855	28.176	21.942	118.530	24.022	5.122	10.651
Diastolic Blood Pressure	22	38.398	84.131	58.568	45.778	69.379	23.601	17.558	58.909	13.340	2.844	5.915
Right Regional Cerebral Oxygen Sat	18	27.333	89.141	62.730	46.873	66.471	19.598	15.561	58.901	16.717	3.940	8.313
Left Regional Cerebral Oxygen Sat	18	36.661	81.523	60.853	49.644	68.017	18.373	13.652	58.984	12.596	2.969	6.264
Jugular Venous Oxygen Saturation	27	48.600	92.872	82.164	78.170	86.990	8.820	6.899	79.814	10.261	1.975	4.059
Brain Oxygen Extraction Fraction	27	1.147	37.783	15.592	11.585	21.394	9.808	7.331	16.941	8.613	1.658	3.407
Avg Regional Cerebral Oxygen Sat	18	33.184	85.332	61.526	48.007	68.206	20.199	15.021	58.980	14.448	3.405	7.185
Central Venous Pressure	20	1.611	31.869	11.255	5.945	15.434	9.490	6.873	11.402	7.552	1.689	3.534
Central Mixed Venous Oxygen Saturation	16	30.087	78.932	69.142	63.584	72.459	8.875	8.030	67.135	11.159	2.790	5.946
Whole Body Oxygen Extraction Fraction	16	20.204	56.221	29.230	27.243	33.460	6.217	5.896	30.670	8.229	2.057	4.385

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3 Extended Display Items

FIGURES

eFigure 1

eFigure 1. Feasibility of data collection in critically ill and medical assistance in dying patient cohorts.

eFigure 2

eFigure 2. Relationships between sedative administration during the dying process and teh resultant length of dying process.

eFigure 3

eFigure 3. Normalized timing signals to demonstrate trajectories throughout the dying process.

eFigure 4

eFigure 5

eFigure 6

Individual AUCs (mmHg s) of hypotensive burden for MAP < 65

ID	AUC_diff
TBI 15	250.10
HIBI 21	547.00
HIBI 10	1112.75
HIBI 32	3613.30
Sepsis 22	3616.35
ICH 17	3654.25
HIBI 2	4255.75
TBI 27	4469.55
Sepsis 1	5914.80
HIBI 12	6227.65
HIBI 24	6352.15
ICH 26	7106.00
HIBI 4	8336.40
HIBI 3	8643.85
TBI 28	8723.95
HIBI 7	10063.25
SAH 23	13385.45
Sepsis 19	17207.09
HIBI 11	21739.20
HIBI 16	50437.20
Sepsis 31	54123.65
Sepsis 14	80295.90
TBI 18	92738.10
ICH 20	124439.00
Sepsis 9	141358.56
HIBI 8	144524.95
HIBI 29	164452.50
TBI 6	169438.70
a The largely different values is based on both the time and severity of injury

Individual AUCs (mmHg s) of hypotensive burden for MAP < 50

ID	AUC
HIBI 21	427.450
HIBI 10	1198.200
Sepsis 22	2577.150
ICH 17	3737.250
HIBI 32	3972.800
TBI 27	5202.950
HIBI 2	5468.200
HIBI 12	5671.050
ICH 26	6415.350
Sepsis 1	6450.650
HIBI 24	8088.200
TBI 28	8153.288
HIBI 3	8552.950
HIBI 7	9743.350
Sepsis 19	11120.210
HIBI 11	11437.650
HIBI 4	11917.550
SAH 23	21094.050
Sepsis 14	28416.100
HIBI 16	29571.350
Sepsis 31	33264.050
TBI 18	99179.500
Sepsis 9	159146.490
ICH 20	161643.500
HIBI 8	169513.000
HIBI 29	220953.450
TBI 6	286902.550
a The largely different values is based on both the time and severity of injury

eFigure 6E-L. The relationship between area under the curve for hypotensive or hypoxemic burden and change in cerebral gradient biomarkers of the neurovascular unit.

eFigure 7

Data Processing Notes:
The alamar NULISA data is a relative concentration which is normally expressed as log2 data. In order to perform data manipulations such as cerebral gradient analysis (AV; Arterial - Jugular venous concentrations), the data needs to be exponentiated, subtracted, then log transformed back into log2 data. You’ll note that individual AV data looks like nothing is close to zero and somewhat polarized on the x-axis. This is normal as the log2 changes, detectable differences, and some level of measurement error will make it very hard to get values basically right on zero so there will be some stochastic noise there.

The other issue with AV analysis is that it is not easily expressed as a log2 difference for volcano plots which is why the AV data is only presented in eFigure 7 as an exploratory analysis. Regardless, it is a way to determine what might being taken up by the brain or removed from the brain compared to control data which we feel can be an interesting future avenue for exploration.

eFigure 7A. Individual arterial plasma proteomic relationships between healthy control participants and patients prior to withdrawal of life-sustaining treatment.

eFigure 7B. Individual cerebral arterial-to-jugular venous gradient plasma proteomic relationships between healthy control participants and patients prior to withdrawal of life-sustaining treatment.

These cerebral arterial-to-jugular venous gradients are an instantaneous release of biomarkers and will not represent bulk fluxes over time. Consequently, while statistics are one part of the story, they do not give a rate of release for biomarkers over time. To get at rate of release of biomarkers, more timepoints are needed in a short time frame to see how biomarkers are changing over a period of time/period of physiologic stress.

eFigure 7C. Individual arterial plasma proteomic relationships between patients prior to withdrawal of life-sustaining treatment and immediately after systolic blood pressure of <60 mmHg.

TABLES

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4 Extra Individual Data Plots

Individual Data Plots

CBv vs MAP

Time Series Saturation Trends

T_0 denotes WLST, T_1 denotes PEA.

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Appendix & Resources

Abbreviation Library

Metrics	Full Name
\(ABP\)	Arterial blood pressure
\(CBF\)	Cerebral blood flow
\(CPP\)	Cerebral perfusion pressure
\(CVC\)	Cerebrovascular conductance
\(DBP\)	Diastolic blood pressure
\(HIBI\)	Hypoxic ischemic brain injury
\(HR\)	Heart rate
\(ICP\)	Intracranial pressure
\(MAP\)	Mean arterial pressure
\(MCAv\)	Medial cerebral artery velocity
\(O_2EF\)	Oxygen extraction fraction
\(P_{ET}CO_2\)	Partial pressure of end-tidal carbon dioxide
\(P_aCO_2\)	Arterial partial pressure of carbon dioxide
\(P_{bt}O_2\)	Intraparenchymal pressure of oxygen
\(PCAv\)	Posterior cerebral artery velocity
\(rSO_2\)	Regional cerebral oxygen saturation
\(S_{jv}O_2\)	Jugular venous oxygen saturation
\(S_pO_2\)	Pulse oxygen saturation

Biomarkers	Full Name
\(GFAP\)	Glial fibrillary acidic protein
\(UCH-L1\)	Ubiquitin carboxy-terminal hydorlase L1
\(Nf-L\)	Neurofilament-light

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1. jordan.bird@alumni.ubc.ca↩︎

2. mypindersekhon@gmail.com↩︎